sigex.acf | R Documentation |
Background: A sigex model consists of process x = sum y, for stochastic components y. Each component process y_t is either stationary or is reduced to stationarity by application of a differencing polynomial delta(B), i.e. w_t = delta(B) y_t is stationary. We have a model for each w_t process, and can compute its autocovariance function (acf), and denote its autocovariance generating function (acgf) via gamma_w (B). Sometimes we may over-difference, which means applying a differencing polynomial eta(B) that contains delta(B) as a factor: eta(B) = delta(B)*nu(B). Then eta(B) y_t = nu(B) w_t, and the corresponding acgf is nu(B) * nu(B^-1) * gamma_w (B).
sigex.acf(L.par, D.par, mdl, comp, mdlPar, delta, maxlag, freqdom = FALSE)
L.par |
Unit lower triangular matrix in GCD of the component's white noise covariance matrix. |
D.par |
Vector of logged entries of diagonal matrix in GCD of the component's white noise covariance matrix. |
mdl |
The specified sigex model, a list object |
comp |
Index of the latent component |
mdlPar |
This is the portion of param corresponding to mdl[[2]], cited as param[[3]] |
delta |
Differencing polynomial (corresponds to eta(B) in Background) written in format c(delta0,delta1,...,deltad) |
maxlag |
Number of autocovariances required |
freqdom |
A flag, indicating whether frequency domain acf routine should be used. |
Notes: this function computes the over-differenced acgf, it is presumed that the given eta(B) contains the needed delta(B) for that particular component. Conventions: ARMA and VAR models use minus convention for (V)AR polynomials, and additive convention for (V)MA polynomials. SARMA and SVARMA use minus convention for all polynomials.
x.acf: matrix of dimension N x N*maxlag, consisting of autocovariance matrices stacked horizontally
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.